update openvino codes (#7196)

deploy/third_engine/demo_openvino_kpts/README.md

@@ -2,8 +2,12 @@
 
 This fold provides TinyPose inference code using
 [Intel's OpenVINO Toolkit](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html). Most of the implements in this fold are same as *demo_ncnn*.  
-**Recommand** to use the xxx.tar.gz file to install instead of github method, [link](https://registrationcenter-download.intel.com/akdlm/irc_nas/18096/l_openvino_toolkit_p_2021.4.689.tgz).
-
+**Recommand** 
+1. To use the xxx.tar.gz file to install instead of github method, [link](https://registrationcenter-download.intel.com/akdlm/irc_nas/18096/l_openvino_toolkit_p_2021.4.689.tgz).
+2. Your can also deploy openvino with docker, the command is :
+```
+docker pull openvino/ubuntu18_dev:2021.4.1
+```
 
 ## Install OpenVINO Toolkit
 
@@ -59,7 +63,30 @@ source /opt/intel/openvino_2021/bin/setupvars.sh
 
 ## Convert model
 
-   Convert to OpenVINO
+  **1. Conver to onnx**
+
+  Create picodet_m_416_coco.onnx and tinypose256.onnx
+
+  example:
+
+    ```shell
+    modelName=picodet_m_416_coco
+    # export model
+    python tools/export_model.py \
+            -c configs/picodet/${modelName}.yml \
+            -o weights=${modelName}.pdparams \
+            --output_dir=inference_model
+    # convert to onnx
+    paddle2onnx --model_dir inference_model/${modelName} \
+            --model_filename model.pdmodel  \
+            --params_filename model.pdiparams \
+            --opset_version 11 \
+            --save_file ${modelName}.onnx
+    # onnxsim
+    python -m onnxsim ${modelName}.onnx ${modelName}_sim.onnx
+    ```
+
+  **2.Convert to OpenVINO**
 
    ``` shell
    cd <INSTSLL_DIR>/openvino_2021/deployment_tools/model_optimizer
@@ -75,9 +102,11 @@ source /opt/intel/openvino_2021/bin/setupvars.sh
 
    Then convert model. Notice: mean_values and scale_values should be the same with your training settings in YAML config file.
    ```shell
-   python3 mo_onnx.py --input_model <ONNX_MODEL> --mean_values [103.53,116.28,123.675] --scale_values [57.375,57.12,58.395]
+   mo_onnx.py --input_model <ONNX_MODEL> --mean_values [103.53,116.28,123.675] --scale_values [57.375,57.12,58.395] --input_shape [1,3,256,192]
    ```
 
+   **Note: The new version of openvino convert tools may cause error in Resize op. If you has problem with this, please try the version: openvino_2021.4.689**
+
 ## Build
 
 ### Windows
@@ -101,11 +130,41 @@ make
 
 
 ## Run demo
+
 Download PicoDet openvino model [PicoDet openvino model download link](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_416_openvino.zip).
-Download TinyPose openvino model [TinyPose openvino model download link](https://paddledet.bj.bcebos.com/deploy/third_engine/tinypose_256_openvino.zip).
+
+Download TinyPose openvino model [TinyPose openvino model download link](https://bj.bcebos.com/v1/paddledet/deploy/third_engine/demo_openvino_kpts.tar.gz), the origin paddlepaddle model is [Tinypose256](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_enhance/tinypose_256x192.pdparams).
 
 move picodet and tinypose openvino model files to the demo's weight folder. 
 
+Note:
+1. The model output node name may update by new version of paddle\paddle2onnx\onnxsim\openvino, please checkout your own model output node when the code can't find "conv2d_441.tmp_1"\"argmax_0.tmp_0".
+2. If you happened with this error "Cannot find blob with name: transpose_1.tmp_0", it means your picodet model is oldversion. you can modify the below code to fix it.
+
+```
+#picodet_openvino.h line 50-54
+
+  std::vector<HeadInfo> heads_info_{
+      // cls_pred|dis_pred|stride
+      {"transpose_0.tmp_0", "transpose_1.tmp_0", 8},
+      {"transpose_2.tmp_0", "transpose_3.tmp_0", 16},
+      {"transpose_4.tmp_0", "transpose_5.tmp_0", 32},
+      {"transpose_6.tmp_0", "transpose_7.tmp_0", 64},
+  };
+
+  modify to:
+
+  std::vector<HeadInfo> heads_info_{
+    // cls_pred|dis_pred|stride
+    {"save_infer_model/scale_0.tmp_1", "save_infer_model/scale_4.tmp_1", 8},
+    {"save_infer_model/scale_1.tmp_1", "save_infer_model/scale_5.tmp_1", 16},
+    {"save_infer_model/scale_2.tmp_1", "save_infer_model/scale_6.tmp_1", 32},
+    {"save_infer_model/scale_3.tmp_1", "save_infer_model/scale_7.tmp_1", 64},
+  };
+```
+
+3. you can view your onnx model with [Netron](https://netron.app/).
+
 ### Edit file
 ```
 step1:

deploy/third_engine/demo_openvino_kpts/keypoint_detector.cpp

@@ -74,7 +74,7 @@ cv::Mat VisualizeKptsResult(const cv::Mat& img,
 
 void KeyPointDetector::Postprocess(std::vector<float>& output,
                                    std::vector<uint64_t>& output_shape,
-                                   std::vector<int>& idxout,
+                                   std::vector<float>& idxout,
                                    std::vector<uint64_t>& idx_shape,
                                    std::vector<KeyPointResult>* result,
                                    std::vector<std::vector<float>>& center_bs,
@@ -141,7 +141,7 @@ void KeyPointDetector::Predict(const std::vector<cv::Mat> imgs,
   infer_request_.Infer();
 
   InferenceEngine::Blob::Ptr output_blob =
-      infer_request_.GetBlob("save_infer_model/scale_0.tmp_1");
+      infer_request_.GetBlob("conv2d_441.tmp_1");
   auto output_shape = output_blob->getTensorDesc().getDims();
   InferenceEngine::MemoryBlob::Ptr moutput =
       InferenceEngine::as<InferenceEngine::MemoryBlob>(output_blob);
@@ -159,12 +159,15 @@ void KeyPointDetector::Predict(const std::vector<cv::Mat> imgs,
     for (int j = 0; j < output_shape.size(); ++j) {
       output_size *= output_shape[j];
     }
+    
     output_data_.resize(output_size);
     std::copy_n(data, output_size, output_data_.data());
+
   }
 
+
   InferenceEngine::Blob::Ptr output_blob2 =
-      infer_request_.GetBlob("save_infer_model/scale_1.tmp_1");
+      infer_request_.GetBlob("argmax_0.tmp_0");
   auto idx_shape = output_blob2->getTensorDesc().getDims();
   InferenceEngine::MemoryBlob::Ptr moutput2 =
       InferenceEngine::as<InferenceEngine::MemoryBlob>(output_blob2);
@@ -175,7 +178,7 @@ void KeyPointDetector::Predict(const std::vector<cv::Mat> imgs,
     auto minputHolder = moutput2->rmap();
     // Original I64 precision was converted to I32
     auto data = minputHolder.as<const InferenceEngine::PrecisionTrait<
-        InferenceEngine::Precision::I32>::value_type*>();
+        InferenceEngine::Precision::FP32>::value_type*>();
 
     // Calculate output length
     int output_size = 1;

deploy/third_engine/demo_openvino_kpts/keypoint_detector.h

@@ -69,7 +69,7 @@ class KeyPointDetector {
       if (idx == 0) {
         output_info.second->setPrecision(InferenceEngine::Precision::FP32);
       } else {
-        output_info.second->setPrecision(InferenceEngine::Precision::I32);
+        output_info.second->setPrecision(InferenceEngine::Precision::FP32);
       }
       idx++;
     }
@@ -99,14 +99,14 @@ class KeyPointDetector {
   // Postprocess result
   void Postprocess(std::vector<float>& output,
                    std::vector<uint64_t>& output_shape,
-                   std::vector<int>& idxout,
+                   std::vector<float>& idxout,
                    std::vector<uint64_t>& idx_shape,
                    std::vector<KeyPointResult>* result,
                    std::vector<std::vector<float>>& center,
                    std::vector<std::vector<float>>& scale);
 
   std::vector<float> output_data_;
-  std::vector<int> idx_data_;
+  std::vector<float> idx_data_;
   float threshold_;
   bool use_dark_;
 

deploy/third_engine/demo_openvino_kpts/keypoint_postprocess.cpp

@@ -74,11 +74,26 @@ void transform_preds(std::vector<float>& coords,
                      std::vector<float>& scale,
                      std::vector<int>& output_size,
                      std::vector<uint64_t>& dim,
-                     std::vector<float>& target_coords) {
+                     std::vector<float>& target_coords,
+                     bool affine=false) {
+  if (affine) {
     cv::Mat trans(2, 3, CV_64FC1);
     get_affine_transform(center, scale, 0, output_size, trans, 1);
     for (int p = 0; p < dim[1]; ++p) {
-    affine_tranform(coords[p * 2], coords[p * 2 + 1], trans, target_coords, p);
+      affine_tranform(
+          coords[p * 2], coords[p * 2 + 1], trans, target_coords, p);
+    }
+  } else {
+    float heat_w = static_cast<float>(output_size[0]);
+    float heat_h = static_cast<float>(output_size[1]);
+    float x_scale = scale[0] / heat_w;
+    float y_scale = scale[1] / heat_h;
+    float offset_x = center[0] - scale[0] / 2.;
+    float offset_y = center[1] - scale[1] / 2.;
+    for (int i = 0; i < dim[1]; i++) {
+      target_coords[i * 3 + 1] = x_scale * coords[i * 2] + offset_x;
+      target_coords[i * 3 + 2] = y_scale * coords[i * 2 + 1] + offset_y;
+    }
   }
 }
 
@@ -172,7 +187,7 @@ void dark_parse(std::vector<float>& heatmap,
 
 void get_final_preds(std::vector<float>& heatmap,
                      std::vector<uint64_t>& dim,
-                     std::vector<int>& idxout,
+                     std::vector<float>& idxout,
                      std::vector<uint64_t>& idxdim,
                      std::vector<float>& center,
                      std::vector<float> scale,
@@ -187,7 +202,7 @@ void get_final_preds(std::vector<float>& heatmap,
   for (int j = 0; j < dim[1]; ++j) {
     int index = (batchid * dim[1] + j) * dim[2] * dim[3];
 
-    int idx = idxout[batchid * dim[1] + j];
+    int idx = int(idxout[batchid * dim[1] + j]);
     preds[j * 3] = heatmap[index + idx];
     coords[j * 2] = idx % heatmap_width;
     coords[j * 2 + 1] = idx / heatmap_width;

deploy/third_engine/demo_openvino_kpts/keypoint_postprocess.h

@@ -37,7 +37,8 @@ void transform_preds(std::vector<float>& coords,
                      std::vector<float>& scale,
                      std::vector<uint64_t>& output_size,
                      std::vector<int>& dim,
-                     std::vector<float>& target_coords);
+                     std::vector<float>& target_coords,
+                     bool affine);
 void box_to_center_scale(std::vector<int>& box,
                          int width,
                          int height,
@@ -51,7 +52,7 @@ void get_max_preds(std::vector<float>& heatmap,
                    int joint_idx);
 void get_final_preds(std::vector<float>& heatmap,
                      std::vector<uint64_t>& dim,
-                     std::vector<int>& idxout,
+                     std::vector<float>& idxout,
                      std::vector<uint64_t>& idxdim,
                      std::vector<float>& center,
                      std::vector<float> scale,

deploy/third_engine/demo_openvino_kpts/main.cpp

@@ -375,9 +375,9 @@ int main(int argc, char** argv) {
     return -1;
   }
   std::cout << "start init model" << std::endl;
-  auto detector = PicoDet("../weight/picodet_m_416.xml");
+  auto detector = PicoDet("./weight/picodet_m_416.xml");
   auto kpts_detector =
-      new KeyPointDetector("../weight/tinypose256.xml", 256, 192);
+      new KeyPointDetector("./weight/tinypose256_git2-sim.xml", 256, 192);
   std::cout << "success" << std::endl;
 
   int mode = atoi(argv[1]);